32 innovations from Bar-Ilan University, available for licensing, co-investment, or spin-out through BIRAD.
Cohen Haim
To explore the role of protein post-translational modifications on lifespan and healthspan , we developed the PHARAOH computational tool based on the 100-fold differences in longevity within the mammalian class. Analyzing acetylome/phosphorylome and proteome data across 107 mammalian species identified 482 and 695 significant longevity-associated acetylated lysine residues in mice and humans, respectively. In addition, we have recognized 2115 longevity associated p phosphorylations. In regards to acetylations, these sites include acetylated lysines in short-lived mammals that were replaced by permanent acetylation or deacetylation mimickers, glutamine or arginine, respectively, in long-lived mammals. Conversely, glutamine or arginine residues in short-lived mammals were replaced by reversibly acetylated lysine in long-lived mammals. For phosphorylations, site these sites include phosphorylated Serine (S), Tyrosine (Y) and threonine (T) or their replacement to aspartic acid (D) or glutamic acid (E)or Alanine (A) and Y to phenylalanine (Y to F). Pathway analyses of the acetylation sites highlighted the involvement of mitochondrial translation, cell cycle, fatty acid oxidation, transsulfuration, DNA repair and others in longevity. A validation assay showed that substitution of lysine 386 with arginine in mouse cystathionine beta synthase, to attain the human sequence, increased the pro-longevity activity of this enzyme. Likewise, replacing the human ubiquitin-specific peptidase 10 acetylated lysine 714 with arginine as in short-lived mammals, reduced its anti-neoplastic function. These findings provide a computational tool for identifying modifications that control longer healthy life and potential interventions to extend human healthspan.
Keshet Joseph
There is provided a system for computing a secure statistical classifier, comprising: at least one hardware processor executing a code for: accessing code instructions of an untrained statistical classifier, accessing a training dataset, accessing a plurality of cryptographic keys, creating a plurality of instances of the untrained statistical classifier, creating a plurality of trained sub-classifiers by training each of the plurality of instances of the untrained statistical classifier by iteratively adjusting adjustable classification parameters of the respective instance of the untrained statistical classifier according to a portion of the training data serving as input and a corresponding ground truth label, and at least one unique cryptographic key of the plurality of cryptographic keys, wherein the adjustable classification parameters of each trained sub-classifier have unique values computed according to corresponding at least one unique cryptographic key, and providing the statistical classifier, wherein the statistical classifier includes the plurality of trained sub-classifiers.
Levy Oren
We introduce a unique and novel customizable 3D interface for producing scalable, biomimetic artificial reefs (ARs), utilizing real data collected from coral ecosystems. This interface employs 3D technologies, 3D imaging with AI generative models, and 3D printing, to extract core reef characteristics, which can be translated and digitized into a 3D printed reef. The advantages of 3D printing lie in providing customized tools by which to integrate the vital details of natural reefs, such as rugosity and complexity, into a sustainable manufacturing process. This methodology can offer economic solutions for developing both small and large-scale biomimetic structures for a variety of restoration situations, that closely resemble the coral reefs they intend to support. Our method consists of the following steps: 1. 3D photogrammetric scan. 2. Generating 3D models based on the scans and prior knowledge from reefs. 3. Printing the generated 3D models. Artificial reefs are designed to resemble natural reefs to the highest degree possible, maximizing restoration efforts both ecologically and aesthetically. Coral reefs are mapped in 3D by diver-based or platform passed using AUV photogrammetry. This technology enables the production of highly detailed 3D models of the substrate and detection of the sessile organisms that inhabit the reef. Conducting photogrammetric surveys in areas which are designated for reef reformation with our ARs, is highly beneficial, as it identifies which natural reef structures harbor the large biodiversity as well as depicting their numerical composition (diversity) within the reef structure using unique AI. CAD design platforms (i.e., Rhinoceros© and Grasshopper©) create biomimetic or bio-inspired designs, using ceramic 3D printing (3DP). Incorporating our 3D model (images) provides a natural foundation to interactively customize it to fit the needs of any type of reef geographically, depth, etc. or refine the biomimetic design. The 3D models are further analyzed geometrically using advanced data-analysis tools, to extract the general features and characteristics that will lead to a successful AR. We offer plug-ins for designing artificial structures that consolidate algorithms based on the formation of a coral reef structure and our eDNA information that can predict the types of biodiversity it may maintain/accommodate. An eDNA and metabarcoding package is combined to monitor and extract key biological and ecological information about coral reefs, indicating its pivotal potential as an evaluation tool for ARs and reef restoration success. Removable appendages incorporated in the desing of our ARs, will be used for eDNA biomass (organism) surveys, alongside seawater samples, without interfering with the restoration process. As the information extracted from eDNA is broadly expansive, it can be utilized to predict biodiversity outcomes of the 3D printed AR based on the 3D imaged reef. Furthermore, eDNA data can help to understand what characteristics of the 3D modeled reef are related to the diversity of organisms that inhabit it, which guide further the fabrication of the AR. eDNA is a useful tool to observe these hard to identify communities and to understand which species benefit most from the AR structure. This information will be collected to understand the community composition, abundance, and richness, available through eDNA analysis. Data collected from coral reefs, using eDNA and 3D imaging, can reinforce their resilience through establishing baselines, monitoring, and evaluation of restoration activities. Combining these data-acquisition tools with 3DP offers a holistic approach to manufacturing biomimetic ARs that are tailor-made to any coral reef worldwide. Eventually leading to an entirely data-driven interface utilizing parametric design software and machine-learning tools to curate an algorithm for customizing ARs, according to the specific, desired characteristics of the reef, such as reef structure/type, biodiversity, depth, coral morphology, etc. Moreover, the algorithm will automate the optimization of AR designs according to the data it is supplied from eDNA, 3D imaging, and other monitoring surveys. This methodology would make it possible to determine the precise design parameters needed to construct an AR, provide a baseline for the expected biodiversity that could accumulate on 3D printed ARs, and ensure no excess waste in the manufacturing process. The development of sustainable large-scale and long-term projects that can provide key social and economic benefits will be a demand of the future. When marine restoration projects manage to meet these requirements, they are able to achieve restoration goals together with social and economic change. Novelty Point out the novel aspects, of your invention (what is new about it, or what are its new features) in detail. Please emphasize the non-obvious/unpredicted aspects of the invention. The novelty of this invention lies in the fabric of the following ingredients: 1. Using unique and novel AI algorithm to map the local biodiversity to select “hotspot” biomimicry reefs. 2. Using novel generative AI methods to generate 3D models based on 3D photogrammetric scans of the environment. 3. Using a unique and novel cross-examination of the AI analyzing with eDNA – predictive biodiversity of 3D printed reef 4. Using unique and novel translation of 3D photogrammetric scanning of the reef into a 3D printable shape. 5. Using unique and novel algorithm to translate the 3D shape into a movement of the 3D printing of pasty materials, such as clay, aligned with calcium carbonate 4f. Advantages of the Invention Describe the advantages of your invention over the conventional manner for solving the problem, describing how and why your invention does it better: The advantages of the invention are: 1. Biomimicry/natural reef replication 2. High rate of biological success 3. Eco-materials using clay 4. Tailor made for any location. 5. Cost effective with digital manufacturing 6. Integration of a process by using multi-disciplinary approach: 7. Incorporation of reef and environmental characteristics 8. Large-scale solutions using advanced scaling and fabrication
Glickman, Oren
We offer a low-cost, relatively small-size device based on imaging and AI technology that enables assessing key quality indicators of tomato fruits (fruit size, weight, and firmness, as well as total soluble solides (TSS), pH, acidity, Vitamin-C, and lycopene contents in the fruit) without having to collect the fruits and take them to the lab for analyses or even touch the fruits. Moreover, the device can provide quality indicators for many fruits simultaneously from a single shot in a speedy process. The latter feature enables the assessment of quality indicators in many tomato fruits in a very short time. It can be used by (1) various food industry companies that want to use a cheap and speedy process to classify their post-harvest fresh produce per their quality or select the most suitable fruits for their specific purposes (products). The device can also be used by (2) farmers to improve the quality of their fruits by tracking changes in quality indicators along the ripening stages and taking specific actions to influence such quality changes or by just integrating the device into autonomous systems (robots that are being implemented in the agricultural sector these days to make the harvest process an automatic process) for smart harvest by tracking the right stage, according to quality indicators, to pick the fruits. Since our offered solution is relatively cheap, we also see a potential use of this device by (3) consumers and markets that want to know the quality of the fruits they are buying/selling. Finally, since tomato has been employed as a model fruit for the scientific community, particularly in the studies on fruit development, (4) the scientific community dealing with fruit quality can also benefit from such a device because it allows assessing quality indicators in many fruits in a cheap and fast way while the fruits are still on the plant (can be used to study fruit quality changes throughout the ripening process, which is of great interest for many researchers studying environmental and meteorological effects on fruit quality indicators).
Kanter Ido
An underlying mechanism for successful deep learning (DL) with a limited deep architecture and dataset, namely VGG-16 on CIFAR-10, was recently presented based on a quantitative method to measure the quality of a single filter in each layer. In this method, each filter identifies small clusters of possible output labels, with additional noise selected as labels out of the clusters. This feature is progressively sharpened with the layers, resulting in an enhanced signal-to-noise ratio (SNR) and higher accuracy. In this study, the suggested universal mechanism is verified for VGG-16 and EfficientNet-B0 trained on the CIFAR-100 and ImageNet datasets with the following main results. First, the accuracy progressively increases with the layers, whereas the noise per filter typically progressively decreases. Second, for a given deep architecture, the maximal error rate increases approximately linearly with the number of output labels. Third, the average filter cluster size and the number of clusters per filter at the last convolutional layer adjacent to the output layer are almost independent of the number of dataset labels in the range [3, 1,000], while a high SNR is preserved. The presented DL mechanism suggests several techniques, such as applying filter’s cluster connections (AFCC), to improve the computational complexity and accuracy of deep architectures and furthermore pinpoints the simplification of pre-existing structures while maintaining their accuracies.
Weber Ofir
GPU - accelerated Locally Injective Shape Deformation
Keshet Joseph
Predicting glottal closure insufficiency using fundamental frequency contour analysis
Gelbard Roi Moshe
Method and System for Dynamic-Incremental Updating of Similarity Tables for Continuous Real- Time Clustering of Big-Data
